ArticleScientific reports2021
Universal image segmentation for optical identification of 2D materials.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Image processing pipeline for AI-driven nanoparticle megalibrary characterization.Scientific reports · 2026Article
- High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials.Micromachines · 2025Article
- Semantic segmentation in crystal growth process using fake micrograph machine learning.Scientific reports · 2024Article
- When Machine Learning Meets 2D Materials: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning.Nano letters · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
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Registered trials
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